Looking for an easy way to segment large marketing audiences automatically?
The RFM framework is an intuitive way to segment customers using their purchase data. It is different to other targeting models because it’s data-based, highly accurate, and scales to any number of resulting segments easily.
RFM is also fast; it can give you dozens of highly accurate segments in seconds or minutes. For comparison, a single customer avatar takes days to complete.
This is a major advantage, especially for SMEs, and the main reason RFM is growing in popularity right now.
In this article, we’ll explain how the framework makes segmentation easy and intuitive; why it isn’t more popular (yet); how you can use RFM to identify and target important segments like:
- High earners
- Customers with high churn risk
- Lost clients
Let’s start with a look at…
The RFM Framework, Explained
RFM is a way to segment customers by the recency, frequency and monetary value of their purchases. It’s similar to the broader RFE model, which measures engagement instead of monetary value. The difference is that it focuses on customers instead of your whole target audience.
The 3 variables of RFM are very straightforward:
- Recency How long has it been since a customer last bought from you? In some industries, a recent customer is more likely to buy again. In others – say, cars and homes – the opposite is true.
- Frequency How often has a customer bought from your brand during a given week, month, year? Understanding purchase frequency can help you understand past results and predict future ones effectively.
- Monetary Value This can be a customer’s value over a certain period of time, per purchase, or total lifetime spend. In some niches, a customer’s individual monetary value isn’t important because volume is more salient. In other niches – say, real estate or the automotive business – the reverse is true.
Using the framework is simple. First, you allocate each variable a number of possible grades. Then, you collect customer data from various sources, clean it up and plug it into the resulting matrix.
If you’re using Selma.ai, this happens automatically as you receive new data. There’s 0 downtime while you’re processing new statistics. If you’re processing data manually or with a tool like Excel, you may have to spend a day or two rearranging numbers before you get your segments.
Either way, the end result is that you have highly specific segments based on 3 important variables. Even better, you don’t have to crunch numbers to get there, meaning anyone can use the framework; not just data scientists.
But the very best part is that RFM isn’t just an elegant theoretical model. Marketers love the results they get from RFM – and here’s why.
Why Marketers Love RFM
This is where RFM is most useful. For starters, it helps you evaluate which prior customers are likeliest to buy. Second, it helps you identify important customer groups, like:
- Consumers who haven’t shopped with you for a while,
- Consumers who buy at regular intervals, and are therefore more likely to opt into seasonal offers/reminders to re-stock.
- Consumers who have made big purchases, but aren’t your regulars (yet).
Categories like these are low-risk and high-reward. RFM is excellent at finding them – and that’s why marketers love it.
As for why marketers prefer RFM over other frameworks, like the generalist RFE and the more subjective customer avatar model? It’s simple:
- Better performance with very little data. When you use RFM to segment consumers, your marketing messages are more likely to be opened, clicked on and bought from. But unlike most segmentation methods, you only need transactional data to use the framework. This makes RFM instantly appealing if you can’t or won’t get in-depth data on your audience.
- Intuitive, clear way to segment customers. Customer avatars and similar frameworks depend on marketers’ personal opinions and evaluations to get results. RFM is data-driven, which makes it easy to measure the exact effect your campaigns had. Moreover, RFM 3D visualizations help marketers explore their data intuitively:
- More converted opportunities. Sending unnecessary e-mails costs you a marginal amount of money. However, if those e-mails lead to unsubscriptions or you getting marked as spam, you also lose to stand money in sales and brand value. RFM helps prevent that. (This is especially important if you’re selling a high-end product and excellent marketing is expected.)
So why is it that RFM is a relative rarity in the world of marketing?
Why is it that sites like Kissmetrics and growth marketers like Neil Patel don’t cover it while giving attention to other, lesser frameworks?
Why do so many marketers still over-solicit their list and audience, to the point that consumers get annoyed and brand value drops?
The 3 Reasons RFM is Underutilized
The first and most important reason that marketers underuse RFM is difficulty of data processing. A 3-dimensional matrix where each variable is ranked 1-5 will have 125 cells. Filling them up means scraping data, cleaning it, then sorting it. This requires computer science/data analysis skills to do, so the time involved is high – and outsourcing to a data engineer is expensive.
Another problem is the time involved in updating the data. Think back to those 125 segments one more time. Now imagine that every time you want to refresh them with new data, you have to go through all the previous steps again. This is far above and beyond the means of most businesses. Unless your company has a piece of marketing automation technology like Selma.ai, which processes your data automatically, RFM can be exceedingly difficult to use.
Ultimately, these problems are significant, but considering RFM’s potential to improve marketing performance, overcoming them is profitable. Remember – with prudent segmentation, the ROI boost will pay for the extra man-hours and tools you need to use RFM.
Alternatively, do things the easy way by checking out how a virtual marketing assistant like Selma takes all the data processing difficulties out of RFM. This means you can plan and launch a win-back campaign, a retention campaign, or any other strategy in minutes rather than hours or days. On top of that Selma will use its machine learning power to predict the impact of campaigns on your segments and selects the ideal audience for a specific campaign by analyzing the movement of customers through the RFM segments. This will result in a daily overview of possible actions to a targeted audience.
Contact us now and see how easy and intuitive Selma.ai makes targeting. See you there!